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adhyayanrathi01adhyayanrathi01/product-manager-os/skills/analytics/audit-events/SKILL.md

audit-events

Skill2 starsChanged 2 months agoMITView on GitHub

Audit existing product analytics events for correctness, completeness, consistency, identity handling, and analysis readiness. Use when instrumentation seems unreliable, metrics disagree, a feature lacks visibility, or a team wants to verify tracking before analysis.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Verify source readiness, event dictionary authority, identity rules, exclusions, and whether product-flow sampling is permitted.

    line 12
  2. Distinctive:

    Check event triggers, naming, property types, required fields, identity rules, ordering, duplication, and version drift.

    line 21
  3. Distinctive:

    Check whether test, employee, bot, demo, and internal activity can be identified.

    line 22
  4. Distinctive:

    Do not modify instrumentation without explicit approval.

    line 27
  5. Very specific:

    Every field above must appear as a labeled section in the produced output.

    line 36

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Audit Events

<!-- CORE:BEGIN -->

Contract

  • Require the decision or product flow, expected instrumentation, observed source scope, environment, and permission boundary.
  • Verify source readiness, event dictionary authority, identity rules, exclusions, and whether product-flow sampling is permitted.
  • Without observed data or flow access, provide a partial documentation audit and name what remains unverified; do not claim runtime correctness.
  • Emit a reusable evidence packet when the audit informs a broader investigation.
<!-- CORE:END -->

Process

  1. Identify the product flow and decisions the instrumentation must support.
  2. Gather the expected tracking plan, current event dictionary, and observed event data.
  3. Check event triggers, naming, property types, required fields, identity rules, ordering, duplication, and version drift.
  4. Check whether test, employee, bot, demo, and internal activity can be identified.
  5. Compare expected coverage with observed coverage.
  6. Sample important events against the real product flow when access permits.
  7. Classify issues by severity and the decisions they could distort.
  8. Recommend the smallest corrections and validation plan.
  9. Do not modify instrumentation without explicit approval.
<!-- CORE:BEGIN -->

Output

List confirmed coverage, gaps, data-quality risks, affected analyses, recommended fixes, and verification steps.

Required fields: Confirmed coverage|Gaps|Data-quality risks|Affected analyses|Recommended fixes|Verification steps

Every field above must appear as a labeled section in the produced output. Verify with evals/check-output.sh audit-events <artifact>.

<!-- CORE:END -->

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